Sketches have always been one of the important tools for human communication. As it can express some complex human thoughts quickly in a succinct form, the sketch processing algorithm is one of the research hotspots in the filed of computer vision. Currently, the research on sketches mainly focuses on the recognition, retrieval, and completion. As researchers focus on the fine-grained operation of sketches, research on sketch segmentation has also received more and more attention. In recent years, with the development of deep learning and computer vision technology, a large number of sketch segmentation methods based on deep learning have been proposed. Moreover, the accuracy and efficiency of sketch segmentation have also been significantly increased. Nevertheless, sketch segmentation is still a very challenging topic because of the abstraction, sparsity, and diversity of sketches. This study organizes, categorizes, analyzes, and summarizes the sketch segmentation algorithm based on deep learning to solve the above deficiency. Firstly, three basic sketch representation methods and commonly used sketch segmentation datasets are shown. According to the sketch segmentation algorithm prediction results, sketch semantic segmentation, sketch perceptual grouping, and sketch parsing are introduced respectively. Moreover, the evaluation results of sketch segmentation are collected and analyzed on the primary data sets. Finally, the application of sketch segmentation is summarized and the possible future development direction is discussed.